JTE Elms

Writer on biz, tech, #RealEstate 🏡, #homeownership, #personalfinance & #decisionmaking. Fixer homeowner living Plan B. Always have a Plan B.

[JTE Elms @jteelms]

A large prehistoric village 2 miles from Stonehenge and connected to it by a stone-paved track probably housed the builders of the monument. 7 huts from the village have been uncovered by archaeologists & 5 were reproduced for modern visitors. Two are shown below. #shelter

[DDN @DDNintelligence]

Your GPUs should be accelerating AI, not recomputing context 🔄

In this demo, Barak Epstein, Product Manager at Google Cloud, shows how Managed Lustre can be used as a KV cache for inferencing. By saving context outside GPU or TPU memory, teams can retrieve precomputed context, reduce recomputation, and improve accelerator efficiency for large-context AI workloads.

Learn how DDN and Google Cloud are helping teams get more performance and better value from AI Infrastructure 👇

[DDN @DDNintelligence]

LUG 2026 was all about performance, community, and what’s next for Lustre. 👏

DDN joined the Lustre community in Indianapolis for a week of conversations around next-gen HPC, AI infrastructure, and the data performance needed to keep modern workloads moving.

Thanks to everyone who stopped by, joined the discussions, and made the week a success!

#LUG2026

[DDN @DDNintelligence]

DDN’s Wendy Stusrud and ✨Amanda R. Lee✨ have been named to CRN’s 2026 Women of the Channel list! 🏅

The CRN Women of the Channel award recognizes standout women leaders across the IT channel, including vendors, distributors, solution providers, and partner-focused organizations.

For DDN, this recognition highlights the impact Wendy Stusrud and Amanda Lee are making through channel strategy, partner enablement, brand leadership, and AI Infrastructure growth. It also reinforces DDN’s commitment to helping partners and customers succeed as AI adoption accelerates.

Join us in congratulating Wendy and Amanda 👏

[DDN @DDNintelligence]

From proof of concept to production in hours, not months ⚡

In this video, Jason Brown, Senior Director of Product and Solutions Marketing at DDN, shares how the DDN Enterprise AI HyperPOD, built with Supermicro and accelerated by NVIDIA, helps organizations deploy AI Infrastructure that is pre-validated, turnkey, and ready for production. He breaks down how DDN Infinia, NVIDIA AI Enterprise software, and high-density compute come together to support RAG and inference with more than 90% GPU utilization, up to 10x power and cooling savings, 22x faster RAG pipelines, and 18x faster inferencing.

Watch the video to see how DDN, Supermicro, and NVIDIA are helping enterprises move from concept to production faster with AI Infrastructure built to scale.

[DDN @DDNintelligence]

We're thrilled to share that DDN has been recognized in the Award-Winning Products category of the 2026 Artificial Intelligence Excellence Awards. 🏆

This recognition reinforces what we’ve believed all along: breakthrough AI outcomes depend on a data platform built for performance, scale, and real-world impact.

Thank you to the Business Intelligence Group for the honor — and congratulations to the entire DDN team behind it. 👏

[DDN @DDNintelligence]

The GPUs may be the engines of AI factories, but the data layer decides whether they run at full speed or sit idle. 💪

Join DDN and NVIDIA at #NVIDIAGTC for a deep dive into the architecture powering next-generation AI infrastructure.

🎤 Unlocking Data at Scale for AI Factories with NVIDIA & DDN
🗓️ 9:00–9:40 AM

Hear from:
• James Coomer, SVP of Products, DDN
• Jacob Liberman, Director, Enterprise Product, NVIDIA

In this session, they’ll break down how data architecture built on the NVIDIA AI Data Platform reference design drives GPU efficiency, reduces energy and cooling demands, and enables high-performance RAG and multi-modal AI workloads.

If you’re building or operating an AI factory, this conversation will highlight the design patterns, and pitfalls, that determine whether your AI infrastructure scales or stalls.

🎟️ Add this session to your GTC Schedule: https://t.co/Qi6ZGhwcZV

[DDN @DDNintelligence]

What does “data movement” actually mean in AI?

DDN's James Coomer explains how, in most real-world AI environments, GPU performance gets constrained by how fast data can move in and out during every phase: preparing datasets, training models, and running inference. He breaks down how massive AI supercomputers with thousands of GPUs still depend on nonstop data feeds, plus frequent model checkpointing to track progress and recover quickly if anything goes wrong.

Watch the full interview to hear how eliminating data movement friction can unlock more GPU productivity and faster model timelines: https://t.co/jQ2FY4cSXj

[DDN @DDNintelligence]

Your EXAScaler is AI-ready. Join us to learn how to unlock it. 🔐

In this recent webinar, DDN's Joel Kaufman and Stephanie Giard break down what KV Cache actually is, why it matters, and how it impacts real-world performance as teams move from training → inference → agentic AI.

Watch the replay: https://t.co/oo20dCdBpJ

#Webinar

[DDN @DDNintelligence]

Watch as Kevin Cochrane, CMO at Vultr, shares how Vultr’s partnership with DDN drives 30x faster data delivery to training clusters and 15x better power efficiency across their AI Infrastructure.

Learn how Vultr and DDN architect for scale and efficiency, and learn how you can accelerate your own large training workloads. ⬇️

Watch the full video here: https://t.co/BZ7Ya1EPGk

[DDN @DDNintelligence]

If you're running large AI workloads, this session could change your infrastructure strategy. 👇

Join Joel Kaufman (Senior Product Manager, DDN) and Peter Bojanic (Principal Customer Solutions Architect, DDN) for a live #webinar exploring how to enable and optimize KV Cache on EXAScaler.

This session is focused on practical enablement and operational considerations, building directly on the KV Cache demo video.

Reserve your spot now: https://t.co/dFFL8OKK5p

[DDN @DDNintelligence]

65% of AI projects stall from skills gaps. Ready to join the 35% that succeed?

A new survey of 600 IT and business leaders, conducted in collaboration with Cognizant and Google Cloud, shows how top organizations scale AI smarter, not just bigger.

Get the full report to learn the architectures and practices that drive success: https://t.co/0A2wjBrDfh

[DDN @DDNintelligence]

DDN is headed to the @wef in Davos! ✈️

Our President and Co-founder, Paul Bloch, will join “Beyond the Hype: Delivering the AI Intelligence Factory,” a session moderated by @McKinsey, alongside leaders from @NVIDIA and Aleria.

The discussion will focus on what it takes to move from AI hype to execution, including real-world impact, strategic adoption, and tangible value creation at scale.

#WEF26

[JTE Elms @jteelms]

https://t.co/X8SlfH3jYx

[DDN @DDNintelligence]

Ignite next-level enterprise AI and HPC with DDN’s AI400X3 Series. 🔥

The AI400X3 Series is part of DDN’s next-generation data intelligence platform, purpose-built to meet the demands of the most intensive AI model training, and HPC environments.

Featuring cutting-edge performance, streamlined deployment, and seamless scalability, the AI400X3 is the platform of choice for NVIDIA SuperPODs and enterprise leaders worldwide.

Learn more: https://t.co/RqsrrMUu72

[DDN @DDNintelligence]

Scale your AI the smart way with DDN HyperPOD 👏

Paul McLeod of @Supermicro outlines HyperPOD as a core data center building block connecting GPU servers with DDN and Infinia systems. It offers configurations from 4 to 32 GPU servers, with Supermicro handling networking and full integration to accelerate HPC and AI rollouts. The approach reduces complexity and aligns with modern AI Infrastructure.

Explore architecture options, validated configurations, and how to request a tailored quote, then see how HyperPOD with DDN advances your AI Infrastructure.

Get the full session: https://t.co/v4mhQEbF36

[DDN @DDNintelligence]

Up to 1 TB/s for cloud AI pipelines, delivered.

Kirill Tropin from Google Cloud shares why Google Cloud partnered with DDN and built Google Cloud Managed Lustre on EXAScalar. It is fully integrated with Google Cloud and Google Cloud Storage, POSIX compliant with a 99.9% SLA, and delivers up to 1 TB/s with millions of IOPS. It scales from 9 TB to 8 PB so teams can start small and grow fast.

Watch to learn how this boosts AI Infrastructure, streamlines data movement, and takes you from proof of concept to production.

Watch the full session: https://t.co/poIwUduE8c

[DDN @DDNintelligence]

AI token factories fail when data becomes the bottleneck, driving token costs up, power efficiency down, and stalling the path to revenue.

DDN Fixes this. If you’re building AI at scale, let’s talk.

[DDN @DDNintelligence]

What if you could get file-system speed with object-store simplicity?

Sven Oehme, CTO at DDN, shares how Infinia delivers interactive performance you expect from file systems while keeping the operational ease of object interfaces. He explains how this approach streamlines AI Infrastructure pipelines and reduces complexity for teams running demanding workloads.

Watch the clip to learn how Infinia can accelerate your AI initiatives ⬇️

[DDN @DDNintelligence]

"[DDN] announced Sovereign AI Blueprints based upon NVIDIA-aligned reference designs."

"The sovereign-by-design architectures are aimed at helping governments and other organizations that need to restrict where their data resides," Forbes' Thomas Coughlin reports.

Learn why it matters ⬇️ https://t.co/GXwhTkBucc